The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
The amount of information archived on computer storage is constantly increasing. Keeping track and sorting through that amount of information can be incredibly time-consuming for even the most advanced computer systems without systems and methods for optimizing search parameters in order to speed up searches.
U.S. Pat. No. 7,310,055 B2 to Odagiri teaches a system that classifies character strings into groups of character strings with the same leading n characters (e.g. “abc”). Odagiri finds the character string with the highest appearance frequency and extracts it from each group, and then registers that character string in a dictionary as initial values. While Odagiri's system compresses the data, Odagiri's system is not very fast, since each search through Odagiri's compressed tree requires several hash table lookup commands.
US RE 041152 to Reynar teaches an adaptive compression technique that pre-fills compression dictionaries before the beginning of data compression with letter sequences, words and/or phrases that are frequent in the domain from which the data being compressed is drawn. The pre-filled dictionary could then be applied to Lempel-Ziv compression techniques in order to speed up compressing and searching. Reynar's compression schema, however, requires the system to already have foreknowledge of the system to pre-fill the dictionary, and also requires multiple hash table lookup commands in order to search through the compressed structure.
U.S. Pat. No. 8,156,156 B2 to Ferragina et al. teaches a method of structuring and compressing labeled trees of an arbitrary degree and shape in order to optimize the size of the tree. Ferragina compresses a labeled tree into two coordinated arrays, one that captures the structure of the tree, and the other capturing the labels of the tree. Ferragina's arrays, however, require one of the arrays to capture the entire root of a leaf within the array structure, which might increase the speed of searching, but is duplicative and does not compress well.
Thus, there remains a need for a system and method to improve the compression of resident databases to search through and the speed at which such databases are searched.